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Proceedings of the Twentieth International Conference on Machine Learning
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Proceedings of the Twentieth International Conference on Machine Learning
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Abstract:
This paper introduces Correlated-Q (CE-Q) learning, a multiagent Q-learning algorithm based on the correlated equilibrium (CE) solution concept. CE-Q generalizes both Nash-Q and Friend-and-Foe-Q: in general-sum games, the set of correlated equilibria contains the set of Nash equilibria; in constant-sum games, the set of correlated equilibria contains the set of minimax equilibria. This paper describes experiments with four variants of CE-Q, demonstrating empirical convergence to equilibrium policies on a testbed of general-sum Markov games.
ICML
Proceedings of the Twentieth International Conference on Machine Learning